Install Jentic One Beta

Connect the FrameThrower API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the FrameThrower API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fframethrower.ai%2Fframethrower" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fframethrower.ai%2Fframethrower" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with FrameThrower API.

Search the film stills library in plain language by craft attributes

Search by a reference image to find visually matching frames

Search by colour to find frames with a target palette

Browse frames filtered by a craft attribute such as shot size or camera angle

Find frames visually similar to a chosen frame

GET STARTED

Start building with FrameThrower API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use FrameThrower API for, with concrete tasks.

★ Agent-Driven Shot Reference

An AI agent connected through Jentic finds cinematography references for a creative brief by searching the library in plain language, then returning frames with their metadata, colour palettes, and deep links, so a director of photography builds a lookbook without scrubbing films by hand. Because FrameThrower indexes craft attributes, the agent can narrow a search to a specific shot size or lens character.

Search for moody low-key close-ups on long lenses and return the top frames with their colour palettes and deep links

Reference-Image and Colour Search

A visual workflow finds matching frames from a picture or a colour target by searching by reference image or by colour, so an art department can match a mood board to real shots. FrameThrower also finds frames similar to one you already like, so the workflow can pivot from a single good match to a set of related references.

Search by this reference image, then expand the result with frames visually similar to the best match

Film Catalogue Browsing

A discovery workflow browses the film catalogue and reads each film's frames, so an editor can explore a title's visual language shot by shot. Autocomplete suggestions help the workflow resolve a film or attribute before it searches, so a vague query lands on the right title.

List the films in the catalogue and return the frames for a chosen title

Key Endpoints

11 endpoints — the framethrower api searches a film stills library indexed frame by frame on lighting, lens character, shot size, camera angle, colour palette, and mood, so an application can find cinematography references in plain language, by a reference image, or by colour.

METHOD

PATH

DESCRIPTION

POST

/search

Search film stills by text

POST

/search/image

Search by a reference image

POST

/search/color

Search by colour

POST

/similar

Find similar frames

POST

/browse

Filter frames by a craft attribute

GET

/frames

Get frame detail

GET

/films

List the film catalogue

GET

/films/{slug}

Get film detail

POST

/search

Search film stills by text

POST

/search/image

Search by a reference image

POST

/search/color

Search by colour

POST

/similar

Find similar frames

POST

/browse

Filter frames by a craft attribute

GET

/frames

Get frame detail

GET

/films

List the film catalogue

GET

/films/{slug}

Get film detail

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring the FrameThrower API by hand means handling its ft_ bearer token and building three different search request shapes for text, image, and colour. Through Jentic you install once, import FrameThrower from the API Directory, store the token once, and your agent calls it.

Permission scoping

FrameThrower is a read-only search API, so you choose which operations your agent may call: you can allow searching and browsing frames and leave nothing destructive on the table, because there is nothing destructive to grant. Rules bound which operations run, so the agent searches within the surface you allow.

Credential isolation

Your FrameThrower token is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'find film stills by description' or 'search frames by colour', and Jentic returns the matching FrameThrower operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using FrameThrower API through Jentic.

Is there a FrameThrower MCP server?

You don't need an MCP server to give your agent the FrameThrower API. Jentic connects it directly from the API Directory: import it, store your token once, and your agent can search film stills by text, image, or colour straight away, with no extra server to run and no tool definitions sitting in the agent's context.

Can I limit what my agent is allowed to do with the FrameThrower API?

Yes. FrameThrower is a read-only search API, so you can write a rule that allows only the search and browse operations the agent needs and nothing else. Every call the agent makes is logged, so you can see exactly which searches it ran.

What authentication does the FrameThrower API use?

The FrameThrower API authenticates with a bearer personal access token, prefixed ft_, per its OpenAPI spec. Through Jentic the token is stored encrypted by your own instance and added to each request at call time, so it never appears in the agent's prompt or logs.

Can I search frames by colour with the FrameThrower API?

Yes. One operation searches by colour to find frames built around a target palette, alongside text search and reference-image search. Each result carries the frame's colour palette and a deep link, so an agent can rank matches and point back to the source.

What are the rate limits for the FrameThrower API?

The OpenAPI spec does not state request rate limits for the FrameThrower API. For current limits and fair-use guidance, check the provider documentation at https://framethrower.ai.

How do I search for a shot through Jentic?

Import the FrameThrower API from the Jentic API Directory, then have your agent issue a request such as 'find moody night exteriors on wide lenses'. Jentic matches the intent to the text-search operation and returns its input schema so the agent builds the correct request, with your stored token injected at call time. To run it on your own infrastructure, install Jentic One from its GitHub repo.

FrameThrower API

AI ReadinessAI-Aware (B-)61/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLVisionbearer11 EndpointsREST

For Agents

Search a film stills library by text, reference image, or colour, browse by craft attribute, and find similar frames in FrameThrower. Authenticates with a bearer personal access token.

Use for: Search for low-key night interiors shot on anamorphic lenses, Find frames that match this reference image, Search for frames built around a teal and orange palette, Browse wide establishing shots with a high camera angle

Not supported: Does not deliver or license raw image files, edit images, or process video. Use for searching and discovering film stills by visual attributes only.

The FrameThrower API searches a film stills library indexed frame by frame on lighting, lens character, shot size, camera angle, colour palette, and mood, so an application can find cinematography references in plain language, by a reference image, or by colour. You search by text, image, or colour, browse by craft attribute, find frames similar to one you like, and read frame and film detail. Responses carry metadata, thumbnail URLs, colour palettes, and deep links rather than raw image bytes, since the frames are reference material whose rights stay with their owners.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

97%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

100%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: BSignals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

50%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: D-Signals: 4

44%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

73%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

50%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: B+Signals: 1

68%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the FrameThrower API to your agent

Capabilities

Start building with FrameThrower API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

FrameThrower API

Jentic AI Readiness Score

FrameThrower API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness